Capital One’s Enterprise Platforms Technology team is seeking a Machine Learning Engineer 4 to build and scale production AI and ML capabilities.

Design, build, and/or deliver ML models and components for real-world business problems in collaboration with Product and Data Science
Guide ML infrastructure decisions using knowledge of modeling and training tradeoffs, including:
- Model selection
- Data and feature selection
- Model training and hyperparameter tuning
- Dimensionality approaches
- Bias/variance considerations
- Validation strategy
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
Collaborate in a cross-functional Agile team to create and improve software enabling big data and ML applications
Retrain, maintain, and monitor ML models in production
Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
Construct optimized data pipelines to support ML model training and operation
Apply CI/CD best practices, including test automation and monitoring, to support successful deployment of ML models and application code
Maintain well-managed code to reduce vulnerabilities, ensure risk-governed models, and follow Responsible and Explainable AI best practices
Use programming languages including Python, Scala, or Java

Bachelor’s degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
4+ years programming with Python, Java, Golang, or C++
4+ years Machine Learning experience using industry-standard frameworks and libraries, including:
- PyTorch or TensorFlow
- Pandas, NumPy, Scikit-learn
4+ years experience operating large-scale distributed systems (Spark, Ray) to prepare AI/ML data
2+ years deploying and operating ML solutions in production and operating production services in cloud environments (AWS, GCP, Azure), including using Kubernetes for large-scale containerized ML systems

Comprehensive, competitive, and inclusive set of health, financial and other benefits supporting total well-being
Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)

EPTech includes many of Capital One’s most important enterprise platforms
Team focus includes establishing practices for building technology solutions across the company and delivering capabilities that exemplify those practices

Marketing and Messaging team delivers hyper-personalized messages and experiences for customers and prospects
Builds scalable platforms delivering omnichannel messages across owned and paid Adtech channels

Master’s or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or a related field
3+ years optimizing ML algorithms, configurations, and infrastructure
3+ years following software development best practices, including source control, testing, code reviews, and CI/CD
3+ years building resilient software with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and incident response plan preparation
3+ years working with ML techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.), model types (Regression, Classification, Clustering, etc.), model architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and evaluating model accuracy while diagnosing underfitting and overfitting
3+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
1+ years as a technical lead developing ML solutions using industry best practices, patterns, and automation
Authored/co-authored a paper on a ML technique, model, or proof of concept

Capital One will not sponsor a new applicant for employment authorization and will not offer immigration-related support for this position (including H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, O-1, and other forms requiring employer immigration support)

Location: Plano, TX (onsite)
Salary (Plano, TX): USD 179,400 - 204,700 per year
Other location salary ranges (may vary by location):
- McLean, VA: $197,300 - $225,100
- New York, NY: $215,200 - $245,600
- Richmond, VA: $179,400 - $204,700
- San Francisco, CA: $215,200 - $245,600

For accommodations: call 1-800-304-9102 or email [email protected]
For technical support or recruiting process questions: email [email protected]

Machine Learning Engineer 4

Job Description